Quantum Computing and AI Combine for Drug Discovery

GANs are deep-learning neural networks that can produce highly effective generative models for drug discovery and design. They can be used to generate data designed to replicate the data distribution for a particular task. 

The standard GAN model consists of a generator and a discriminator. The generator uses random data noise as input and tries to mimic the data distribution over time, whereas the discriminator tries to distinguish between the fake and real samples. A GAN continuously trains until the discriminator cannot distinguish between the generated and real data.

In this study, researchers gradually replaced each component of MolGAN, an implicit GAN for small molecular

The study demonstrated that the trained quantum GANs could create molecules that resembled those in a training set by employing the VQC as the noise generator. It also showed that the quantum generator outperformed the classical GAN in computing the drug characteristics of generated compounds.

The study also found that the quantum discriminator of GAN, with only tens of learning parameters, could generate valid molecules. It could also outperform its classical counterpart, with tens of thousands of adjustable parameters, in generating molecule properties and the KL-divergence score – a metric that assesses the dissimilarity between the probability distributions of the generated molecules and the target distribution.

In connection with this endeavor, Insilico Medicine published a study in the Journal of Chemical Information and Modeling on accelerating drug discovery and development using breakthrough methods alongside new technologies such as generative AI and quantum computing.